Methods and apparatus for detecting structural, perfusion, and functional abnormalities

ABSTRACT

A method for obtaining data includes scanning a lung of a patient with a Multi-Energy Computed Tomography (MECT) system to acquire data regarding a plurality of contrast agents.

BACKGROUND OF THE INVENTION

This invention relates to computed tomographic (CT) imaging, and moreparticularly to methods and apparatus for the detection and diagnosis oflung abnormalities.

In spite of recent advancements in computed tomography (CT) technology,such as faster scanning speeds, larger coverage with multiple detectorrows, and thinner slices, energy resolution is still a missing piece.Namely, wide x-ray photon energy spectrum from the x-ray source and thelack of energy resolution from CT detection systems preclude energydiscrimination CT.

X-ray attenuation through a given object is not a constant. Rather, theX-ray attenuation is strongly dependent on the x-ray photon energy. Thisphysical phenomenon manifests itself in the image as beam-hardeningartifacts, such as, nonuniformity, shading, and streaks. Somebeam-hardening artifacts can be easily corrected, but otherbeam-hardening artifacts may be more difficult to correct. In general,known methods to correct beam hardening artifacts include watercalibration, which includes calibrating each CT machine to remove beamhardening from materials similar to water, and iterative bonecorrection, wherein bones are separated in the first-pass image thencorrecting for beam hardening from the bones in the second-pass.However, beam hardening from materials other than water and bone, suchas metals and contrast agents, may be difficult to correct. In addition,even with the above described correction methods, conventional CT doesnot provide quantitative image values. Rather, the same material atdifferent locations often shows different CT numbers.

Another drawback of conventional CT is a lack of materialcharacterization. For example, a highly attenuating material with a lowdensity can result in the same CT number in the image as a lessattenuating material with a high density. Thus, there is little or noinformation about the material composition of a scanned object is basedsolely on the CT number. At least some state-of-the-art CT scannerscurrently available are limited to providing anatomical information. Forlung scans, images produced by such scanners exhibit a significant levelof image artifacts and CT number inaccuracy. These limitations preventthe utilization of the CT device for advanced diagnosis. Accordingly,the methods and apparatus described herein address the detection anddiagnosis of lung abnormalities.

BRIEF DESCRIPTION OF THE INVENTION

In one aspect, a method for obtaining data is provided. The methodincludes scanning a lung of a patient with a Multi-Energy ComputedTomography (MECT) system to acquire data regarding a plurality ofcontrast agents

In another aspect, a Multi-Energy Computed Tomography (MECT) System isprovided. The MECT includes a radiation source, a radiation detector,and a computer operationally coupled to the radiation source and theradiation detector. The computer is configured to receive data regardinga first energy spectrum of a scan of a lung of a patient, receive dataregarding a second energy spectrum of the scan of the lung, generating afirst functional image using data regarding a first contrast agent, andgenerating a second functional image using data regarding a secondcontrast agent.

In yet another aspect, a Multi-Energy Computed Tomography (MECT) Systemis provided. The MECT includes a radiation source, a radiation detector,and a computer operationally coupled to the radiation source and theradiation detector. The computer is configured to receive data regardinga first energy spectrum of a scan of a lung of a patient, receive dataregarding a second energy spectrum of the scan, and decompose thereceived data to generate data regarding a plurality of contrast agents.

In still another aspect, a computer readable medium is encoded with aprogram. The program is configured to instruct a computer to receivedata regarding a first energy spectrum of a scan of a lung of a patient,receive data regarding a second energy spectrum of the scan, anddecompose the received data to generate data regarding a plurality ofcontrast agents.

In yet still another aspect, a computer readable medium is encoded witha program. The program is configured to instruct a computer to scan alung of a patient with a Multi-Energy Computed Tomography (MECT) systemto acquire data regarding a first contrast agent in a gaseous medium anda second contrast agent in a liquid medium, generate a first functionalimage using data regarding the first contrast agent, and generate asecond functional image using data regarding the second contrast agent.

In another aspect a method for obtaining data is provided. The methodincludes administering a gaseous contrast agent to a patient,administering a liquid contrast agent to the patient, and imaging thepatient to obtain data regarding the gaseous contrast agent and theliquid contrast agent.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a pictorial view of a MECT imaging system.

FIG. 2 is a block schematic diagram of the system illustrated in FIG. 1.

FIG. 3 is a flow chart representing a pre-reconstruction analysis.

FIG. 4 is a flow chart representing a post-reconstruction analysis.

FIG. 5 is a schematic view of Lung Ventilation and Perfusion systems.

FIG. 6 illustrates examples of ventilation perfusion maps.

FIG. 7 illustrates an example where a V/Q map has light gray areas thatindicate ventilation is less than perfusion.

FIG. 8 illustrates an example where areas of very dark color indicatethat perfusion is less than ventilation.

DETAILED DESCRIPTION OF THE INVENTION

The methods and apparatus described herein address the detection anddiagnosis of abnormalities in the lung regions of a patient by employingnovel approaches that make use of basic properties of the x-ray andmaterial interaction. For each ray trajectory, multiple measurementsregarding different mean x-ray energies are acquired. As explained ingreater detail below, when Basis Material Decomposition (BMD) andCompton and photoelectric decomposition are performed on thesemeasurements, additional information is obtained that enables improvedaccuracy and characterization.

In some known CT imaging system configurations, an x-ray source projectsa fan-shaped beam which is collimated to lie within an X-Y plane of aCartesian coordinate system and generally referred to as an “imagingplane”. The x-ray beam passes through an object being imaged, such as apatient. The beam, after being attenuated by the object, impinges uponan array of radiation detectors. The intensity of the attenuatedradiation beam received at the detector array is dependent upon theattenuation of an x-ray beam by the object. Each detector element of thearray produces a separate electrical signal that is a measurement of thebeam intensity at the detector location. The intensity measurements fromall the detectors are acquired separately to produce a transmissionprofile.

In third generation CT systems, the x-ray source and the detector arrayare rotated with a gantry within the imaging plane and around the objectto be imaged such that the angle at which the x-ray beam intersects theobject constantly changes. A group of x-ray attenuation measurements,i.e., projection data, from the detector array at one gantry angle isreferred to as a “view”. A “scan” of the object comprises a set of viewsmade at different gantry angles, or view angles, during one revolutionof the x-ray source and detector.

In an axial scan, the projection data is processed to construct an imagethat corresponds to a two-dimensional slice taken through the object.One method for reconstructing an image from a set of projection data isreferred to in the art as the filtered backprojection technique. Thisprocess converts the attenuation measurements from a scan into integerscalled “CT numbers” or “Hounsfield units” (HU), which are used tocontrol the brightness of a corresponding pixel on a cathode ray tubedisplay.

To reduce the total scan time, a “helical” scan may be performed. Toperform a “helical” scan, the patient is moved while the data for theprescribed number of slices is acquired. Such a system generates asingle helix from a fan beam helical scan. The helix mapped out by thefan beam yields projection data from which images in each prescribedslice may be reconstructed.

Reconstruction algorithms for helical scanning typically use helicalweighing algorithms that weight the collected data as a function of viewangle and detector channel index. Specifically, prior to a filteredbackprojection process, the data is weighted according to a helicalweighing factor, which is a function of both the gantry angle anddetector angle. The weighted data is then processed to generate CTnumbers and to construct an image that corresponds to a two-dimensionalslice taken through the object.

To further reduce the total acquisition time, multi-slice CT has beenintroduced. In multi-slice CT, multiple rows of projection data areacquired simultaneously at any time instant. When combined with helicalscan mode, the system generates a single helix of cone beam projectiondata. Similar to the single slice helical, weighting scheme, a methodcan be derived to multiply the weight with the projection data prior tothe filtered backprojection algorithm.

As used herein, an element or step recited in the singular and proceededwith the word “a” or “an” should be understood as not excluding pluralsaid elements or steps, unless such exclusion is explicitly recited.Furthermore, references to “one embodiment” of the present invention arenot intended to be interpreted as excluding the existence of additionalembodiments that also incorporate the recited features.

Also as used herein, the phrase “reconstructing an image” is notintended to exclude embodiments of the present invention in which datarepresenting an image is generated but a viewable image is not. However,many embodiments generate (or are configured to generate) at least oneviewable image.

Herein are described methods and apparatus for detecting structural,perfusion, and functional abnormalities in lung tissue using anenergy-discriminating (also known as multi-energy) computed tomography(MECT) system. First described is MECT system 10 and followed by lungapplications using MECT system 10.

Energy Discrimination (Multi-Energy) CT System 10

Referring to FIGS. 1 and 2, a multi-energy scanning imaging system, forexample, a multi-energy multi-slice computed tomography (MECT) imagingsystem 10, is shown as including a gantry 12 representative of a “thirdgeneration” CT imaging system. As used herein, a multi-energy computedtomography system may also be referred to as an energy discrimination CT(EDCT) system. Gantry 12 has an x-ray source 14 that projects a beam ofx-rays 16 toward a detector array 18 on the opposite side of gantry 12.Detector array 18 is formed by a plurality of detector rows (not shown)including a plurality of detector elements 20 which together sense theprojected x-rays that pass through an object, such as a medical patient22. Each detector element 20 produces an electrical signal thatrepresents the intensity of an impinging x-ray beam and hence can beused to estimate the attenuation of the beam as it passes through objector patient 22. During a scan to acquire x-ray projection data, gantry 12and the components mounted therein rotate about a center of rotation 24.FIG. 2 shows only a single row of detector elements 20 (i.e., a detectorrow). However, multi-slice detector array 18 includes a plurality ofparallel detector rows of detector elements 20 such that projection datacorresponding to a plurality of quasi-parallel or parallel slices can beacquired simultaneously during a scan.

Rotation of components on gantry 12 and the operation of x-ray source 14are governed by a control mechanism 26 of MECT system 10. Controlmechanism 26 includes an x-ray controller 28 that provides power andtiming signals to x-ray source 14 and a gantry motor controller 30 thatcontrols the rotational speed and position of components on gantry 12. Adata acquisition system (DAS) 32 in control mechanism 26 samples analogdata from detector elements 20 and converts the data to digital signalsfor subsequent processing. An image reconstructor 34 receives sampledand digitized x-ray data from DAS 32 and performs high-speed imagereconstruction. The reconstructed image is applied as an input to acomputer 36, which stores the image in a storage device 38. Imagereconstructor 34 can be specialized hardware or computer programsexecuting on computer 36.

Computer 36 also receives commands and scanning parameters from anoperator via console 40 that has a keyboard. An associated cathode raytube display 42 allows the operator to observe the reconstructed imageand other data from computer 36. The operator supplied commands andparameters are used by computer 36 to provide control signals andinformation to DAS 32, x-ray controller 28, and gantry motor controller30. In addition, computer 36 operates a table motor controller 44, whichcontrols a motorized table 46 to position patient 22 in gantry 12.Particularly, table 46 moves portions of patient 22 through gantryopening 48.

In one embodiment, computer 36 includes a device 50, for example, afloppy disk drive, CD-ROM drive, DVD drive, magnetic optical disk (MOD)device, or any other digital device including a network connectingdevice such as an Ethernet device for reading instructions and/or datafrom a computer-readable medium 52, such as a floppy disk, a CD-ROM, aDVD, a MOD or an other digital source such as a network or the Internet,as well as yet to be developed digital means. Computer 36 is programmedto perform functions described herein, and as used herein, the termcomputer is not limited to just those integrated circuits referred to inthe art as computers, but broadly refers to computers, processors,microcontrollers, microcomputers, programmable logic controllers,application specific integrated circuits, and other programmablecircuits, and these terms are used interchangeably herein. CT imagingsystem 10 is an energy-discriminating (also known as multi-energy)computed tomography (MECT) system in that system 10 is configured to beresponsive to different x-ray spectra. This can be accomplished with aconventional third generation CT system to acquire projectionssequentially at different x-ray tube potentials. For example, two scansare acquired either back to back or interleaved in which the tubeoperates at 80 kVp and 160 kVp potentials, for example. Alternatively,special filters are placed between the x-ray source and the detectorsuch that different detector rows collect projections of different x-rayenergy spectrum. Alternatively, the special filters that shape the x-rayspectrum can be used for two scans that are acquired either back to backor interleaved. Yet another embodiment is to use energy sensitivedetectors such that each x-ray photon reaching the detector is recordedwith its photon energy. Although the specific embodiment mentioned aboverefers to a third generation CT system, the methods described hereinequally apply to fourth generation CT systems (stationarydetector—rotating x-ray source) and fifth generation CT systems(stationary detector and x-ray source).

There are different methods to obtain multi-energy measurements: (1)scan with two distinctive energy spectra, (2) detect photon energyaccording to energy deposition in the detector, and (3) photon counting.Photon counting provides clean spectra separation and an adjustableenergy separation point for balancing photon statistics.

MECT facilitates reducing or eliminating a plurality of problemsassociated with conventional CT, such as, but not limited to, a lack ofenergy discrimination and material characterization. In the absence ofobject scatter, one only need system 10 to separately detect two regionsof photon energy spectrum: the low-energy and the high-energy portionsof the incident x-ray spectrum. The behavior at any other energy can bederived based on the signal from the two energy regions. This phenomenonis driven by the fundamental fact that in the energy region wheremedical CT is interested, two physical processes dominate the x-rayattenuation: (1) Compton scatter and the (2) photoelectric effect. Thus,detected signals from two energy regions provide sufficient informationto resolve the energy dependence of the material being imaged.Furthermore, detected signals from two energy regions provide sufficientinformation to determine the relative composition of an object composedof two materials.

In an exemplary embodiment, MECT uses a decomposition algorithm, suchas, but not limited to, a CT number difference algorithm, a Compton andphotoelectric decomposition algorithm, a basis material decomposition(BMD) algorithm, and a logarithm subtraction decomposition (LSD)algorithm.

The CT number difference algorithm includes calculating a differencevalue in a CT or a Hounsfield number between two images obtained atdifferent tube potentials. In one embodiment, the difference values arecalculated on a pixel-by-pixel basis. In another embodiment, average CTnumber differences are calculated over a region of interest. The Comptonand photoelectric decomposition algorithm includes acquiring a pair ofimages using MECT 10, and separately representing the attenuations fromCompton and photoelectric processes. The BMD algorithm includesacquiring two CT images, wherein each image represents the equivalentdensity of one of the basis materials. Since a material density isindependent of x-ray photon energy, these images are approximately freeof beam-hardening artifacts. Additionally, an operator can choose thebasis material to target a certain material of interest, thus enhancingthe image contrast. In use, the BMD algorithm is based on the conceptthat the x-ray attenuation (in the energy region for medical CT) of anygiven material, can be represented by proper density mix of other twogiven materials, accordingly, these two materials are called the basismaterials. In one embodiment, using the LSD algorithm, the images areacquired with quasi-monoenergetic x-ray spectra, and the imaged objectcan be characterized by an effective attenuation coefficient for each ofthe two materials, therefore the LSD algorithm does not incorporatebeam-hardening corrections. Additionally, the LSD algorithm is notcalibrated, but uses a determination of the tissue cancellationparameters, which are the ratio of the effective attenuation coefficientof a given material at the average energy of each exposure. In anexemplary embodiment, the tissue cancellation parameter is primarilydependent upon the spectra used to acquire the images, and on anyadditional factors that change the measured signal intensity from thatwhich would be expected for a pair of ideal, mono-energetic exposures.

It should be noted that in order to optimize a multi-energy CT system,the larger the spectra separation, the better the image quality. Also,the photon statistics in these two energy regions should be similar,otherwise, the poorer statistical region will dominate the image noise.

Lung Applications of Energy Discriminating using Multi-Energy CT System10

The present invention applies the above principle to lung studies. Inspecific, MECT system 10 is utilized to produce CT images as hereindescribed. A pre-reconstruction analysis, a post-reconstructionanalysis, and a scout image analysis are three techniques that can beused with MECT system 10 for tissue characterization.

FIG. 3 is a flow chart representing a pre-reconstruction analysis 54wherein a decomposition 56 is accomplished prior to a reconstruction 58.Computer 36 collects the acquired projection data generated by detectorarray 18 (shown in FIG. 1) at discrete angular positions of the rotatinggantry 12 (shown in FIG. 1), and passes the signals to a preprocessor60. Preprocessor 60 re-sorts the projection data received from computer36 to optimize the sequence for the subsequent mathematical processing.Preprocessor 60 also corrects the projection data from computer 36 fordetector temperature, intensity of the primary beam, gain and offset,and other deterministic error factors. Preprocessor 60 then extractsdata corresponding to a high-energy view 62 and routes it to a highenergy channel path 64, and routes the data corresponding to alow-energy views 66 to a low energy path 68. Using the high energy dataand low energy data, a decomposition algorithm is used to produce twostreams of projection data, which are then reconstructed to obtain twoindividual images pertaining to two different materials.

FIG. 4 is a flow chart representing a post-reconstruction analysiswherein decomposition 56 is accomplished after reconstruction 58.Computer 36 collects the acquired projection data generated by detectorarray 18 (shown in FIG. 1) at discrete angular positions of rotatinggantry 12 (shown in FIG. 1), and routes the data corresponding tohigh-energy views 62 to high energy path 64 and routes the datacorresponding to low-energy views 66 to low energy path 68. A first CTimage 70 corresponding to the high-energy series of projections 62 and asecond CT image 72 corresponding to low-energy series of projections 66are reconstructed 58. Dual-energy decomposition 56 is then performedusing a decomposition algorithm to obtain two individual imagesrespectively, pertaining to two different materials. In scout imageanalysis, the signal flow can be similar to FIG. 3 or FIG. 4. However,the table is moved relative to the gantry to acquire the data.

One common cause of a ventilation perfusion mismatch is pulmonaryembolization. However, any cause of pulmonary artery obstruction(bronchogenic carcinoma, lymphoma, metastatic disease, sarcoma,aneurysm, sarcoid, and fungal or granulomatous infection) can producethe same findings. In some known clinical practices,ventilation-perfusion scans are acquired with limited spatial resolutionusing positron emission tomography (PET) requiring the injection ofradioactive isotopes. Similarly, dual scans are required which presentdifficulty in registering anatomy between the two scans, especially inthe case where the scan times or scan interval is long enough to createmotion-related artefacts. Multiple energy computed tomography (MECT)ventilation-perfusion maps are generated by simultaneously injecting IVcontrast, such as an ionic or non-ionic iodine-based (I) agent such asIopamidol, chelates of gadolinium such as Gd-DTPA, or non-ionic chelatessuch as gadodiamide (gadolinium-diethylenetriamine pentaacetic acidbismethylamide, C₁₆H₂₈GdN₅O₉xH₂O), to a patient while he/she inhales astable non-radioactive gas usually having a high atomic number and ordensity such as Xe¹³¹. A single scan is acquired and the ratio ofHounsfield units in CT pixel data reconstructed at each of multipleenergies provides a 3-dimensional ventilation-perfusion map. Theresulting image has significant clinical value. Two major applicationsare detection of pulmonary emboli and assessment of regional lungfunction. When a blood clot blocks a pulmonary artery, blood flow ceasesto the lung region normally supplied by that vessel, and a corresponding“perfusion defect” results. Such a defect manifest itself as a “high”signal in a ventilation/perfusion map as described below (V/Q map)indicating an area where oxygen exchange is inefficient.

In use, and in accordance with one embodiment, a patient breathes onefull breath of Xe¹³¹ and a substantially simultaneous (timed) Bolusinjection of a contrast medium (I) is done. Multiple Energy CT scan datais collected with system 10 at Total Lung Capacity (TLC). The collecteddata is decomposed using one of the decomposition methods describedabove (CT Number Difference, Compton and Photoelectric Decomposition,Basis Material Decomposition, or Logarithm Subtraction Decomposition),MECT images are acquired with x-ray spectra or energy discrimination tohighlight the differences between the relevant atoms of the administeredcontrast agents (eg: Xe¹³¹ and I) The decomposition step yields twoimage data sets, one (the ventilation functional image, “V” image)emphasizing the density of Xe¹³¹; the other (the perfusion functionalimage, “Q” image) illustrating the density of I.

After the two functional images are generated, a V/Q signal from theimages is calculated. In one embodiment, the two functional images areregistered with each other and a V/Q signal is calculated on apixel-by-pixel basis, wherein the ratio of CT numbers in the V image tothe CT numbers of the Q image represents the V/Q signal for eachlocation in the two images. The V/Q map can be presented by displayingthe 3-dimensional lung anatomy via a traditional grayscale image withthe V/Q ratios superimposed using a colormap. The traditional grayscaleimage is an anatomical image and not a functional image. This providesan anatomical map of oxygen exchange efficiency. Using any combinationof the acquired images, V, Q, and/or V/Q map data, an observer orcomputer algorithm can highlight regions of suspicious V/Q. A computeraided detection or diagnosis algorithm can use the data to determinediagnosis of pathologies such as Chronic Obstructive Pulmonary Disease(COPD) and emphysema, or detect, quantify, and classify pathologicalregions of poor pulmonary function.

FIG. 5 is a schematic view of Lung Ventilation and Perfusion systemsshowing an Arterial/Venous network on the left side of FIG. 5 and anAirway/Bronchial network on the right side of FIG. 5. Abnormalities ineither of these networks will result in V/Q mismatch, ratiosinconsistent with the overall V/Q activity of the entire pulmonaryanatomy.

FIG. 6 illustrates examples of ventilation perfusion maps including aPerfusion Image showing a vascular network on the left side, aVentilation Image with bronchial and alveolar network illustrated in themiddle, and a ratio of Middle to Left image (V/Q Map) on the right sideof FIG. 6. A medium gray area indicates ventilation and perfusion arematched. FIG. 7 illustrates an example where the V/Q map has light grayareas that indicate ventilation is less than perfusion and potentialrespiratory obstruction may exist. Similarly, FIG. 8 illustrates anexample where areas of very dark color indicate that perfusion is lessthan ventilation and a potential for arterial blockage or pulmonaryembolism may exist.

The herein described methods and apparatus facilitate characterizinglung tissue which facilitates the diagnosis of abnormalities in anefficient and cost effective manner.

While the invention has been described in terms of various specificembodiments, those skilled in the art will recognize that the inventioncan be practiced with modification within the spirit and scope of theclaims.

What is claimed is:
 1. A method for obtaining data, said method comprising scanning a lung of a patient with a Multi-Energy Computed Tomography (MECT) system to substantially simultaneously acquire data regarding a plurality of contrast agents.
 2. A method in accordance with claim 1 further comprising: generating a first functional image using data regarding a first contrast agent; and generating a second functional image using data regarding a second contrast agent.
 3. A method in accordance with claim 1 further comprising: administering a first contrast agent in a gaseous state to the patient; and administering a second contrast agent in a liquid state to the patient.
 4. A method in accordance with claim 3 wherein said administering a first contrast agent comprises administering a first contrast agent comprising Xenon.
 5. A method in accordance with claim 4 wherein said administering a second contrast agent comprises administering a second contrast agent comprising Iodine or Gadolinium.
 6. A method in accordance with claim 1 further wherein said scanning comprises scanning the patient at Total Lung Capacity (TLC).
 7. A method in accordance with claim 1 wherein to generate the first and second images, said method further comprises decomposing the acquired data.
 8. A method in accordance with claim 7 further comprising performing a Basis Material Decomposition (BMD) of the acquired data.
 9. A method in accordance with claim 7 further comprising performing a Computed Tomography (CT) Difference Decomposition of the acquired data.
 10. A method in accordance with claim 7 further comprising performing a Compton and Photoelectric Decomposition of the acquired data.
 11. A method in accordance with claim 7 further comprising performing a Logarithm Subtraction Decomposition of the acquired data.
 12. A method for obtaining data, said data method comprising: scanning a lung of a patient with a Multi-Energy Computer Tomography (MECT) system to acquire data regarding a plurality of contrast agents; generating a first functional image using data regarding a first contrast agent; generating a second functional image using data regarding a second contrast agent; and registering the first functional image with the second functional image.
 13. A method in accordance with claim 12 further comprising generating a ratio map on a pixel by pixel basis between the registered first and second images.
 14. A method in accordance with claim 13 further comprising displaying the ratio map in color as an overlay to a grayscale anatomical image of the lung.
 15. A Multi-Energy Computed Tomography (MECT) System comprising: a radiation source; a radiation detector; and a computer operationally coupled to said radiation source and said radiation detector, said computer configured to: receive data regarding a first energy spectrum of a scan of a lung of a patient; receive data regarding a second energy spectrum of the scan of said lung; generating a first functional image using data regarding a first contrast agent; and generating a second functional image using data regarding a second contrast agent.
 16. A MECT system in accordance with claim 15 wherein said computer further configured to perform a Compton and photoelectric decomposition of the received data.
 17. A MECT system in accordance with claim 15 wherein said computer further configured to perform a Basis Material Decomposition (BMD) of the received data.
 18. A MECT system in accordance with claim 15 wherein said computer further configured to perform a Computed Tomography (CT) Difference Decomposition of the received data.
 19. A MECT system in accordance with claim 15 wherein said computer further configured to perform a Logarithm Subtraction Decomposition of the received data.
 20. A MECT system in accordance with claim 15 wherein said computer further configured to generate a ratio map on a pixel by pixel basis between the registered first and second images.
 21. A MECT system in accordance with claim 20 wherein said computer further configured to display the ratio map in color as an overlay to a grayscale anatomical image of the lung.
 22. A Multi-Energy Computed Tomography (MECT) System comprising: a radiation source; a radiation detector; and a computer operationally coupled to said radiation source and said radiation detector, said computer configured to: receive data regarding a first energy spectrum of a scan of a lung of a patient; receive data regarding a second energy spectrum of the scan; and decompose the received data to generate data regarding a plurality of contrast agents.
 23. A computer readable medium encoded with a program configured to instruct a computer to: receive data regarding a first energy spectrum of a Computer Tomography (CT) scan of a lung of a patient; receive data regarding a second energy spectrum of the CT scan; and decompose the received data to generate data regarding a plurality of contrast agents.
 24. A computer readable medium encoded with a program configured to instruct a computer to: scan a lung of a patient with a Multi-Energy Computed Tomography (MECT) system to acquire data regarding a first contrast agent in a gaseous medium and a second contrast agent in a liquid medium; generate a first functional image using data regarding the first contrast agent; and generate a second functional image using data regarding the second contrast agent.
 25. A method for obtaining data, said method comprising: administering a gaseous contrast agent to a patient; administering a liquid contrast agent to the patient; and imaging the patient to obtain data regarding the gaseous contrast agent and the liquid contrast agent in a single Computer Tomography (CT) data acquisition process.
 26. A method in accordance with claim 25 wherein said imaging comprises imaging the patient to substantially simultaneously obtain data regarding the gaseous contrast agent and the liquid contrast agent in a single data acquisition process. 